An embodiment of the invention relates to a method for the registration or resetting of an image applied to digital subtracted angiography. The applicable field of the invention is that of medical imaging.
Angiography is the term applied to techniques used for the acquisition of images of blood vessels in an object or body, typically a patient's body. During the acquisition, the blood is opacified by means of a contrast agent making the vascular system visible in the direction of applied radiation, typically X-rays. Radiography is used for the acquisition of the angiography sequences. X-rays are sent out at a given rate and the images are digitized and then stored on a storage disk.
X-ray angiography is currently the reference examination in therapeutic vascular neuro-radiology. To be able to make a diagnosis, a physician must be able to extract information on the patient's vascular system from photographs of the patient. However, the blood vessels and blood have an absorption index that is substantially the same as that of the surrounding tissues.
Digital Subtracted Angiography (DSA) is a technique that substantially overcomes this limitation. The operational mode for DSA is as follows. A contrast agent, typically an iodide-based contrast agent, is injected into the patient's blood vessel. This agent is highly radio-opaque and enables a reading of the contrast in the vascular tissues. The angiography examination is based on the acquisition of two sequences. A first sequence is made without any contrast agent; it is known as a mask image sequence. A second sequence following the first sequence is made after the injection of the contrast agent; it is known as a sequence of opacified images or contrast images. These two sequences therefore ideally differ only by the presence of opacified blood vessels. A subtraction between these images then reveals the payload information.
However, owing to the patient's movements, both externally (the non-immobility of the patient, respiration, swallowing motions, etc.) and internally (cardiac motion, heart pulse, intestinal gases, etc.), the subtracted angiography images have artifacts that adversely affect the quality of the medical diagnosis. To eliminate these artifacts entirely, it is necessary to devise an automatic, robust and precise technique to correct the patient's movements. In this method, before subtraction, the mask image is registered relative to the opacified image by the application of local geometrical deformations to it. In one local example, these deformations are determined by the maximizing of a criterion of similarity.
With a rigid registration module, for each image that the user wishes to register, the user chooses a rectangular zone that will serve as an ROI (Region of Interest). A shift vector is then computed between the mask image and the corresponding contrast image on the basis of the chosen zone. The computation method is explained further below. This single vector is used to register the entire image.
Since the registration is rigid it therefore generally enables efficient registration only in the selected region. Indeed, the patient's movement is rarely uniform: two structures near the heart and the lungs may be located in a same image and will be respectively be influenced to a greater degree by the heartbeat and respiration. Furthermore, since the acquisitions are 2D projections of 3D structures, the motions of a near structure may be different from those of a distant structure. Nor can the rotational motions of the body be taken into account. This often results in the creation of troublesome artifacts outside the region of interest. The practitioner can therefore focus on only one small zone at a time. Furthermore, if the size of the region of interest is too great, artifacts may be created within this very zone, because the surface is then too great for a measurement of similarity to be significant. These deteriorations in image quality are not acceptable.
Another limitation is the obligation for the user to choose the region of interest, and the time that this operation takes. Indeed, for a many patients, a large number of images and the choice of several zones per image, this action of choosing the ROI becomes considerable.
Finally, if the measurement of similarity is unstable, the registration is not done. This situation can arise if the user has selected a homogeneous zone, where computation is impossible. This safety wall is a good thing in itself because it prevents the application of a wrong shift to the entire image but does not make possible to register all the zones desired. In this case, there is a further loss of time.
In view of these limitations, another solution was found to the problem of registration. This solution had to give a subtracted image of higher quality. Non-rigid or elastic registration algorithms were then proposed. The basic principle of these non-rigid algorithms is the search, relative to each pixel of the image, for an associated shift vector, for example by maximizing a criterion of similarity for each pixel. The computation of the shifts for each pixel by criterion of similarity is however very costly in computation time. Meijering et al., in “Image Enhancement in Digital X-Ray Angiography”, Image Sciences Institute, Utrecht, Netherlands, chap. 2, 2000, has furthermore shown that another technique, known as the optical flow technique, is not applicable in the case of digital subtracted angiography.
To reduce computation time, a reduced set of control points, or landmarks, is then selected and a shift vector is then estimated for these control points. These shifts are then propagated to all the pixels of the image by interpolation. The computation time is then reduced.
The choice of the landmarks is then preferably done on the mask image in order to prevent a situation where one of them does not belong to the vascular system, and is thus properly revealed only in the contrast image and therefore does not appear in the mask image. The landmarks may be chosen in two ways: either by means of a regular spatial distribution (a grid), or by a definition according to their characteristics (uneven distribution).
The first method does not take account of the particular features of the image. The homogeneous zones of the image therein possess landmarks, whereas for them the shift vector is impossible to estimate while the highly non-homogeneous zones do not have enough landmarks therein to make a precise assessment of the shift vector.
The second method enables this problem to be resolved through an adequate and robust distribution of the landmarks. As a criterion of robustness, it is possible to choose a criterion of the gradient. Indeed, it can be seen that the subtraction artifacts appear in digital subtracted angiography in zones that are dense in structures.
For the computation of the shift vector, each landmark has an associated region of interest on which the computation of similarity will be done. It has been shown that the best results, taking account of the computation time as well as the visual quality, were obtained for a region of interest sized 48×48 pixels.
However, certain problems of registration remain. X-ray imaging comprises projections of a 3D image on a 2D image. This may prompt the appearance, between two acquisitions, of structures that have been concealed beforehand. Another problem is that of isophotes, namely lines of equal light intensity, such as those corresponding to the edge of a bone. The shifting of a pixel of this line is indeterminate if this motion is parallel to the isophote. Indeed, the question is how to determine the new position of a point among neighbors having the same intensity.
Another major problem persists with the method in which the choice of landmarks is made as a function of their characteristics. This problem is that of the determining of the landmarks concerned for the computation of interpolation of the shift of a pixel. In practice, for a pixel for which the shift has to be computed, it is necessary to determine all three landmarks forming the smallest possible triangle around this pixel. This determining process is very lengthy because, in principle, it is possible to envisage many candidate landmarks. For example if, by chance, four candidate landmarks are distributed at the four corners of a square, there are four triangles, hence four possible ways, of determining pixel shift vectors within the square.